Motion Planning Under Uncertainty for Image-guided Medical Needle Steering
- 1 November 2008
- journal article
- research article
- Published by SAGE Publications in The International Journal of Robotics Research
- Vol. 27 (11-12), 1361-1374
- https://doi.org/10.1177/0278364908097661
Abstract
We develop a new motion planning algorithm for a variant of a Dubins car with binary left/right steering and apply it to steerable needles, a new class of flexible bevel-tip medical needle that physicians can steer through soft tissue to reach clinical targets inaccessible to traditional stiff needles. Our method explicitly considers uncertainty in needle motion due to patient differences and the difficulty in predicting needle/tissue interaction. The planner computes optimal steering actions to maximize the probability that the needle will reach the desired target. Given a medical image with segmented obstacles and target, our method formulates the planning problem as a Markov decision process based on an efficient discretization of the state space, models motion uncertainty using probability distributions and computes optimal steering actions using dynamic programming. This approach only requires parameters that can be directly extracted from images, allows fast computation of the optimal needle entry point and enables intra-operative optimal steering of the needle using the pre-computed dynamic programming look-up table. We apply the method to generate motion plans for steerable needles to reach targets inaccessible to stiff needles, and we illustrate the importance of considering uncertainty during motion plan optimization.Keywords
This publication has 28 references indexed in Scilit:
- The Optimal Timing of Living-Donor Liver TransplantationManagement Science, 2004
- Needle insertion modeling and simulationIEEE Transactions on Robotics and Automation, 2003
- Curvature-Constrained Shortest Paths in a Convex PolygonSIAM Journal on Computing, 2002
- MR Compatible Surgical Assist Robot: System Integration and Preliminary Feasibility StudyLecture Notes in Computer Science, 2000
- Control of systems integrating logic, dynamics, and constraintsAutomatica, 1999
- A unified framework for hybrid control: model and optimal control theoryIEEE Transactions on Automatic Control, 1998
- Planning shortest bounded-curvature paths for a class of nonholonomic vehicles among obstaclesJournal of Intelligent & Robotic Systems, 1996
- Planning under time constraints in stochastic domainsArtificial Intelligence, 1995
- Learning to act using real-time dynamic programmingArtificial Intelligence, 1995
- An optimal one-way multigrid algorithm for discrete-time stochastic controlIEEE Transactions on Automatic Control, 1991